“Let’s Train Humans First…Before We Train Machines”

We humans are at the absurd stage in our technological evolution when we seem to have abandoned our common sense. Billions are spent by governments, corporations and investors in training computer-based algorithms (i.e. computer programs) in today’s mindless rush to create so-called “artificial” intelligence, widely advertised as AI. Meanwhile, training our children and their brains (already superior to computer algorithms) is under-funded, schools are dilapidated, sited in run-down, often polluted areas while our teachers are poorly paid and need greater respect. How did our national priorities get so skewed?

In reality, there is nothing artificial about these algorithms or their intelligence, and the term “AI” is a mystification! The term that describes the reality is “Human-Trained Machine Learning”, in today’s mad scramble to train these algorithms to mimic human intelligence and brain functioning. In the techie magazine WIRED, October 2018, we meet a pioneering computer scientist, Fei-Fei LI, testifying at a Congressional hearing, who underlines this truth. She said, “Humans train these algorithms” and she talked about the horrendous mistakes these machines make in mis-identifying people, using the term “bias in—bias out” updating the old computer saying, “garbage in—garbage out”.

Professor LI described how we are ceding our authority to these algorithms to judge who gets hired, who goes to jail, who gets a loan, a mortgage or good insurance rates — and how these machines code our behavior, change our rules and our lives. She is now back at Stanford University after a time as an ethicist at Google and has started a foundation to promote the truth about AI, since she feels responsible for her role in inventing some of these algorithms herself. As a celebrated pioneer of this field, Professor LI says “There’s nothing artificial about AI. It’s inspired by people, it’s created by people and more importantly, it impacts people”.

So how did Silicon Valley invade our culture and worldwide technology programs with its short-term, money -obsessed values: “move fast and break things”; disrupt the current systems while rushing to scale and cash out with an IPO? These values are discussed by two insiders in shocking detail, by Antonio G. Martinez in “Chaos Monkeys” (2016) and Bloomberg’s Emily Chang in “Brotopia” (2018). These authors explain a lot about how training these algorithms went so wrong: subconsciously mimicking their mostly male, misogynist, often white entrepreneurs and techies with their money-making monopolistic biases and often adolescent, libertarian fantasies.

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